Uncertainty aware parameter provision for a variational quantum algorithm

ABSTRACT

Systems, computer-implemented methods and/or computer program products that can facilitate providing a defined parameter, determining whether to employ the defined parameter for a variational quantum algorithm, and running the variational quantum algorithm on a quantum system, are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a decision component that determines, based upon an uncertainty prediction regarding the usability of the defined parameter that has been output from a machine learning model, whether to employ the defined parameter in a variational quantum algorithm, such as run on a quantum system.

BACKGROUND

One or more embodiments herein relate generally to providing a definedparameter for a variational quantum algorithm and more specifically, toalso determining whether to employ the defined parameter for thevariational quantum algorithm and providing subsequent relatedinstruction regarding supplementary parameter optimization.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments described herein. This summary is not intended toidentify key or critical elements, or to delineate any scope of theparticular embodiments or any scope of the claims. The sole purpose ofthe summary is to present concepts in a simplified form as a prelude tothe more detailed description that is presented later. In one or moreembodiments described herein, devices, systems, computer-implementedmethods, apparatus and/or computer program products are described thatcan facilitate providing a defined parameter, determining whether toemploy the defined parameter for a variational quantum algorithm, andoperating the variational quantum algorithm on one or more qubits on aquantum system.

According to an embodiment, a system can comprise a memory that storescomputer executable components and a processor that executes thecomputer executable components stored in the memory. The computerexecutable components can comprise a decision component that determines,based upon an uncertainty prediction regarding the usability of adefined parameter that has been output from a machine learning model,whether to employ the defined parameter for running a variationalquantum algorithm.

According to another embodiment, a computer-implemented method cancomprise determining, by a system operatively coupled to a processor,and based upon an uncertainty prediction regarding the usability of adefined parameter that has been output from a machine learning model,whether to employ the defined parameter for running a variationalquantum algorithm.

According to yet another embodiment, a computer program productfacilitating providing a defined parameter and determining whether toemploy the defined parameter for a variational quantum algorithm, cancomprise a computer readable storage medium having program instructionsembodied therewith. The program instructions can be executable by aprocessor to determine, by the processor, and based upon an uncertaintyprediction regarding the usability of the defined parameter that hasbeen output from a machine learning model, whether to employ the definedparameter for running a variational quantum algorithm.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemthat can facilitate providing a defined parameter, determining whetherto employ the defined parameter for a variational quantum algorithm, andoperating the variational quantum algorithm on one or more qubits on aquantum system, in accordance with one or more embodiments describedherein.

FIG. 2 illustrates another block diagram of an example, non-limitingsystem that can facilitate providing a defined parameter, determiningwhether to employ the defined parameter for a variational quantumalgorithm, and operating the variational quantum algorithm on one ormore qubits on a quantum system, in accordance with one or moreembodiments described herein.

FIG. 3 illustrates yet another block diagram of an example, non-limitingsystem that can facilitate providing a defined parameter, determiningwhether to employ the defined parameter for a variational quantumalgorithm, and operating the variational quantum algorithm on one ormore qubits on a quantum system, in accordance with one or moreembodiments described herein.

FIG. 4 illustrates still another block diagram of an example,non-limiting system that can facilitate providing a defined parameter,determining whether to employ the defined parameter for a variationalquantum algorithm, and operating the variational quantum algorithm onone or more qubits on a quantum system, in accordance with one or moreembodiments described herein.

FIG. 5 illustrates a flow diagram of an example, non-limitingcomputer-implemented method that can facilitate providing a definedparameter, determining whether to employ the defined parameter for avariational quantum algorithm, and operating the variational quantumalgorithm on one or more qubits on a quantum system, in accordance withone or more embodiments described herein.

FIG. 6 illustrates a continuation of the flow diagram of FIG. 5 , of anexample, non-limiting computer-implemented method that can facilitateproviding a defined parameter, determining whether to employ the definedparameter for a variational quantum algorithm, and operating thevariational quantum algorithm on one or more qubits on a quantum system,in accordance with one or more embodiments described herein.

FIG. 7 illustrates a continuation of the flow diagram of FIG. 5 , of anexample, non-limiting computer-implemented method that can facilitateproviding a defined parameter, determining whether to employ the definedparameter for a variational quantum algorithm, and operating thevariational quantum algorithm on one or more qubits on a quantum system,in accordance with one or more embodiments described herein.

FIG. 8 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated.

FIG. 9 illustrates a block diagram of an example, non-limiting cloudcomputing environment in accordance with one or more embodimentsdescribed herein.

FIG. 10 illustrates a block diagram of a plurality of example,non-limiting abstraction model layers, in accordance with one or moreembodiments described herein.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in this Detailed Description section.

Quantum algorithms are those that employ a quantum system, such as aquantum computer or other quantum device, to attempt to solve a problem,such as of an optimization nature. One type of quantum algorithm, avariational quantum algorithm, relies on one or more parameterizedquantum circuits, yielding one or more parameterized wavefunctions. Thevariational quantum algorithm, or VQA, employs an optimizer, such as aclassical optimizer, to train the one or more parameterized quantumcircuits for the purpose of approximating one or more answers to one ormore given problems. A parameterized quantum circuit is one thatcomprises one or more gates having parameters that allow for fine-tuningof an effect of implementing the parameterized quantum circuit. Theparameterized quantum circuit can be operated on one or more qubits onthe quantum system. The parameters are employed to compile one or moreparticular waves and/or pulses, for example, for affecting the state ofone or more of the qubits on the quantum system.

Employing suitable, e.g., optimal, parameters when running a VQA canmaximize the VQA's cost function. That is, employing optimal parameterscan lower the cost of the solution provided, such as in terms of any oneor more of energy, time and/or fidelity, thus making the VQA efficientand reducing errors during implementation of the VQA. What makes aparameter respectively optimal can be dependent on the particular VQA tobe run, such as a parameter being a low energy parameter or a lowfidelity parameter.

For example, a goal of one type of VQA, can be to maximize a costfunction in terms of energy. A goal of another type of VQA can be tofacilitate approximation of the action of a first quantum circuit over agiven wave function based upon the action of a second quantum circuitover the given wave function. In one or more cases, the second quantumcircuit can be shallow, parameter dependent and/or hardware-efficient.The approximation of the action of the first quantum circuit can beaccomplished by maximizing fidelity between states of the first andsecond quantum circuits.

Some examples of VQAs can include a quantum approximate optimizationalgorithm (QAOA), a variational quantum eigensolver (VQE), a variationalquantum simulation of time evolution (VarITE), a variational quantumdeflation (VQD), and algorithms for classically assisted quantum circuitsimplification. The QAOA and VQE can be concerned with minimizing theenergy of a quantum state. The VQD can be concerned with the sum of theenergy of a quantum state and its overlap with the ground state of thesystem. The VarITE can produce curves in the parameter space to minimizethe expectation value of an actual function and thus can approximatesreal- or imaginary-time evolution. Algorithms for classically assistedquantum circuit simplification can maximize the fidelity between atarget quantum state and the output of the quantum circuit.

Embodiments of one or more systems, computer-implemented methods,apparatuses and/or computer program products described herein can enablethe maximizing of a cost function for a VQA, such as via facilitatingthe initial determination, optimization, testing, and in one or morecases, further optimization and testing, of one or more parameters,i.e., variational quantum parameters. The parameters can define, such asbeing proportional to an integral of a curve of, an electromagneticpulse sent to qubits in the associated quantum system. Parameters can beoptimized classically, external to the one or more quantum systemsemployed for implementing the VQA. Accordingly, one or more systemsdescribed herein can include both a classical system and a quantumsystem, thereby being a hybrid system.

Via the one or more embodiments described herein, initial values for theparameters for employing a VQA can be determined at least partiallymanually, such as by combining physical intuition and mathematicalarguments, and/or through use of an Ansatz method. These parameters canbe optimized using a classical algorithm, such as a gradient descent,including evaluation of the respective cost function or other suitableoperator, and in one or more cases, first and/or second derivativesthereof. The parameters can be tested on a quantum system, such as aquantum computer, to determine whether they are suitable, e.g., optimal,for use in running the VQA on the quantum system.

This process, however, can be undesirably costly in terms of time andenergy and can take days, weeks or even months to provide one or moreparameters that are optimal for running the VQA. That is, thisoptimization procedure can be unacceptably expensive, in terms of one ormore of energy, time and fidelity, and generally can represent acomputational bottleneck in the implementation of a VQA.

In one example, quantum computing cloud service providers can executemillions of quantum-related jobs for one or more entities during a giventime period, such as a year. As used herein, an “entity” can refer to amachine, device, component, hardware, software, smart device or human.This can create pressure to execute respective quantum programs quickly,for example, to maximize system usage and/or to minimize compiling timeto compile quantum programs. This in turn can result in entities havingto wait for the compiling to be completed and can undesirably consumeclassical computational resources that could be otherwise employed.Furthermore, pressure can be created to execute these jobs well, suchthat the most performance can be extracted from near-term error-pronesystems and/or such that the quality of compiling into physical-levelpulses, e.g., electromagnetic pulses, can be improved.

Because of the high computational cost of parameter provision (includinginitial determination and/or one or more optimization iterations),and/or in view of the growing presence of VQAs in contemporary quantumcomputing, one or more embodiments described herein can facilitatebypassing and/or accelerating parameter optimization. That is, one ormore embodiments described herein can include one or more systems,computer-implemented methods, apparatuses and/or computer programproducts that can facilitate providing a defined parameter, determiningwhether to employ the defined parameter for a variational quantumalgorithm, and operating the variational quantum algorithm on one ormore qubits on a quantum system.

For example, the one or more systems, computer-implemented methodsand/or computer program products can facilitate: determination of aparameter; determination of usability of the defined parameter; use ofthe defined parameter in a VQA where an uncertainty meets one or moreuncertainty levels; optimization of the defined parameter for supplyinga resultant supplementary parameter where the uncertainty meets one ormore uncertainty levels but does not meet one or more other uncertaintylevels; non-use of the parameter in the VQA where the uncertainty doesnot meet one or more uncertainty levels; and/or determination and/oroptimization of the defined parameter or a supplementary parameteremploying an Ansatz-based optimization method.

These processes can provide increased efficiency and scaled parameteroptimization and/or implementation of quantum algorithms. Further, theone or more systems, computer-implemented methods, apparatuses and/orcomputer program products can facilitate updating of a central datastore with the defined parameter and/or the related uncertainty leveland/or updating of the central data store from a plurality of systemsbeing distributed relative to one another. These updating processes canin turn enable better training of associated machine learning models forsubsequent parameter provision, thereby facilitating faster futureparameter provision and further increased scaling where the central datastore continues to be updated.

One or more of the aforementioned embodiments are now described withreference to the figures, where like referenced numerals are used torefer to like elements throughout. In the following description, forpurposes of explanation, one or more specific details are set forth inorder to provide a more thorough understanding of the one or moreembodiments. It is evident in one or more cases, however, that the oneor more embodiments can be practiced without these specific details.

Further, it should be appreciated that the embodiments depicted in oneor more figures disclosed herein are for illustration only, and as such,the architecture of embodiments is not limited to the systems, devicesand/or components depicted therein, nor to any particular order,connection and/or coupling of systems, devices and/or componentsdepicted therein. For example, in one or more embodiments, thenon-limiting system 100 and/or the parameter provision system 102 canfurther comprise one or more computer and/or computing-based elementsdescribed herein with reference to an operating environment 800illustrated at FIG. 8 . In several described embodiments, computerand/or computing-based elements can be used in connection withimplementing one or more of the systems, devices, components and/orcomputer-implemented operations shown and described in connection withFIG. 1 or with other figures disclosed herein.

Turning now in particular first to FIG. 1 , the figure illustrates ablock diagram of an example, non-limiting system 100. Generally, thenon-limiting system 100 can provide an optimal parameter forimplementing a VQA on one or more qubits on a quantum system, and/or canachieve and/or attempt to achieve an objective of a VQA based at leastin part on the provision of the optimal parameter.

The non-limiting system 100 can comprise a parameter provision system102 that can facilitate providing a defined parameter and determiningwhether to employ the defined parameter for a variational quantumalgorithm and/or related processes, in accordance with one or moreembodiments described herein. Parameter provision system 102 cancomprise any type of component, machine, device, facility, apparatusand/or instrument that comprises a processor and/or can be capable ofeffective and/or operative communication with a wired and/or wirelessnetwork. All such embodiments are envisioned. For example, parameterprovision system 102 can comprise a server device, computing device,general-purpose computer, special-purpose computer, quantum computingdevice (e.g., a quantum computer), tablet computing device, handhelddevice, server class computing machine and/or database, laptop computer,notebook computer, desktop computer, cell phone, smart phone, consumerappliance and/or instrumentation, industrial and/or commercial device,digital assistant, multimedia Internet enabled phone, multimedia playersand/or another type of device.

In one or more embodiments, the parameter provision system 102 cancomprise a processor 104 (e.g., computer processing unit,microprocessor, classical processor, quantum processor and/or likeprocessor). In one or more embodiments, any component associated withparameter provision system 102, as described herein with or withoutreference to the one or more figures of the one or more embodiments, cancomprise one or more computer and/or machine readable, writable and/orexecutable components and/or instructions that can be executed byprocessor 104 to facilitate performance of one or more operationsdefined by such component(s) and/or instruction(s).

In one or more embodiments, the parameter provision system 102 cancomprise a computer-readable memory 106 that is operably connected tothe processor 104. The memory 106 can store computer-executableinstructions that, upon execution by the processor 104, can cause theprocessor 104 and/or other components of the parameter provision system102 (e.g., training component 108, data component 110, performancecomponent 112, decision component 114, Ansatz component 116, VQAcomponent 118, updating component 120 and/or aggregation component 122)to perform one or more acts. In one or more embodiments, the memory 106can store computer-executable components (e.g., training component 108,data component 110, performance component 112, decision component 114,Ansatz component 116, VQA component 118, updating component 120 and/oraggregation component 122).

Parameter provision system 102 and/or a component thereof as describedherein can be communicatively, electrically, operatively, opticallyand/or otherwise coupled to one another via a bus 124 to performfunctions of non-limiting system 100, parameter provision system 102and/or any components thereof. Bus 124 can comprise one or more of amemory bus, memory controller, peripheral bus, external bus, local bus,quantum bus and/or another type of bus that can employ one or more busarchitectures. One or more of these examples of bus 124 can be employedto implement any one or more embodiments described herein.

In one or more embodiments, parameter provision system 102 can becoupled (e.g., communicatively, electrically, operatively, opticallyand/or like function) to one or more external systems, sources and/ordevices (e.g., classical and/or quantum computing devices, communicationdevices and/or like devices), such as via a network.

Turning now to detailed explanations of one or more componentsillustrated at FIG. 1 , functionality of the parameter provision system102 will be described in detail. The following description(s) refer(s)to the provision of a single parameter, i.e. variational quantumparameter. However, it will be appreciated that one or more of theprocesses described herein can be scalable. For example, as will beappreciated below, the performance component 112 and/or the decisioncomponent 114 can provide one or more parameters and/or one or moreassociated decisions regarding the parameter subsequently and/orconcurrently relative to one another.

Generally, the parameter provision system 102 can facilitate theuncertainty aware provision of a parameter for use in a VQA, such as ininitialization of the VQA on a quantum system, such as the quantumsystem 130. This parameter provision can be uncertainty aware, such aswhere one or more predictions regarding the usability of the definedparameter also can be provided. In one instance, the usability can berelated to low energy such that an ideal parameter is an increasinglylow energy parameter as compared to previously defined parameters. Inanother instance, the usability can be related to low fidelity.Generally, where a threshold of usability is met, the parameterprovision system 102 can make a decision to bypass parameteroptimization, such as via an optimization portion 202 of thenon-limiting system 100 (FIG. 2 ), or to utilize the defined parameteras an initial parameter for supplementary, but accelerated, parameteroptimization, also such as via the optimization portion 202 and/orsimilar. Further, by providing parameters having high usability, such asbeing low energy or low fidelity parameters, the VQA can be run moreefficiently, using less energy and/or time, for example, and thusresulting in respectively reduced error during operation on one or morequbits of a quantum system. Additionally, through the use of fewerresources for optimizing parameter values for subsequent VQA operations,the non-limiting system 100 can increase the throughput of thenon-limiting system 100.

In addition to the processor 104 and/or memory 106 described above,parameter provision system 102 can comprise one or more computer and/ormachine readable, writable and/or executable components and/orinstructions that, when executed by processor 104, can facilitateperformance of one or more operations defined by such component(s)and/or instruction(s). In one or more embodiments, parameter provisionsystem 102 can comprise a training component 108, data component 110,performance component 112, decision component 114, Ansatz component 116,VQA component 118, updating component 120 and/or aggregation component122. In one or more instances, such as described in detail below,parameter provision system 102 can facilitate via processor 104 (e.g., aclassical processor, a quantum processor and/or like processor):training a machine learning model; executing the machine learning modelto provide an uncertainty prediction and a parameter; employing anAnsatz-based optimization method to optimize a parameter; determining,based upon the uncertainty prediction, whether to employ the definedparameter in a VQA; and/or operating the variational quantum algorithmemploying the defined parameter.

For example, the parameter provision system 102 can comprise a trainingcomponent 108. Before initialization of the variational quantumalgorithm (VQA) 119 on a quantum system 130, such as a quantum computer,data related to the VQA 119 can be pulled from one or more respectivedatabases, such as the central data store 109. The data pulled caninclude historical data regarding previous iterations of running the VQA119, previous parameters utilized, previous uncertainty predictionsand/or the like. The central data store 109 can be any suitable databasefor storing data and can be directly or indirectly communicativelyconnected to the parameter provision system 102. In one or moreinstances, the central data store 109 can be accessible via a network,such as the cloud. In one or more instances, data can be otherwisereceived by the training component 108.

Upon obtaining the aforementioned data, the training component 108 cantrain a machine learning (ML) model 111 with respect to theaforementioned data for providing the defined parameter 113. It will beappreciated that the ML model 111 can be one of a plurality of ML modelsthat can be formulated for facilitating parameter provision for avariational quantum algorithm.

The ML model 111 can be AI-supported through, for example, but notlimited to, automated hyperparameter tuning and/or model selection.

The ML model 111 can be trained (e.g., by the training component 108) tobe uncertainty aware, such as by basing the ML model 111 on a nativelyuncertainty aware machine learning algorithm, for example, but notlimited to, Gaussian processes and Bayesian neural networks, and/orthrough using one or more methods known to those skilled in the art fordetermining uncertainty from non-natively uncertainty aware machinelearning models, for example, but not limited to, ensemble analysis andcalibration models.

That is, the ML model 111 can be trained (e.g., by the trainingcomponent 108) to provide a prediction regarding the usability of thedefined parameter 113 relative to the VQA 119. In one instance, theusability can be related to low energy such that an ideal parameter isan increasingly low energy parameter as compared to previous definedparameters. In another instance, the usability can be related to lowfidelity such that an ideal parameter is an increasingly low fidelityparameter as compared to previous defined parameters. One or morethresholds of the usability can be default and/or selectivelyestablished, such as by an entity accessing the parameter provisionsystem 102, by the training component 108 and/or by the decisioncomponent 114.

A data component 110 can store the trained machine learning (ML) model111 and/or information relating thereto, such as metadata. In one ormore embodiments, an ML model 111 stored at the data component 110 canbe an ML model that has been determined to be desirable (e.g., suitableor optimal) for storage in the data component 110, such as being an MLmodel 111 selected by an entity as providing usable parameters for usein one or more particular types of variational quantum algorithms In oneor more embodiments, an ML model 111 can be stored at the central datastore 109 and/or at any other storage device accessible by the parameterprovision system 102.

The performance component 112 can operate the ML model 111 to providethe defined parameter 113 as a result. Likewise the performancecomponent 112 can operate the ML model 111 to provide an uncertaintyprediction 115 related for the defined parameter 113 and relative to theVQA 119. That is, in view of the ML model 111 being trained by thetraining component 108 with data specific to the VQA 119, theuncertainty prediction 115 can therefore be related to one or moreaspects, goals and/or objectives of the VQA 119. The uncertaintyprediction 115 can be a quantitative representation of the usability ofthe defined parameter 113 relative to the VQA 119. The uncertaintyprediction 115 can be provided in any suitable format, such as apercentage, a number along a range and/or any other suitable format,such as related to the type of threshold set.

The decision component 114 can determine, based on the uncertaintyprediction 115, how to proceed with the defined parameter 113. Thefunction of the decision component 114 will be described in greaterdetail with respect to FIGS. 2 to 4 . That is, the decision component114 can provide a decision whether or not to utilize the definedparameter 113 for the VQA 119. For example, the decision component 114can make a decision including: (a) to proceed with completelysupplementary parameter optimization (e.g., Ansatz-based optimization)without employing the defined parameter 113 (e.g., FIG. 2 ); (b) toproceed with additional parameter optimization (e.g., Ansatz-basedparameter optimization) using the defined parameter 113 as an initialstart point (e.g., FIG. 3 ); or (c) to proceed without additionalparameter optimization (e.g., FIG. 4 ).

As described herein, the decision component 114 can make such decisionrelative to at least two uncertainty thresholds, as will be explainedbelow in detail. A first uncertainty threshold can be met to allow forcompletely bypassing parameter optimization. A second, and lower,uncertainty threshold can be met to allow for use of the definedparameter 113 as an initial start point for performing the parameteroptimization. Additional and/or alternative thresholds can be utilizedwhere suitable in one or more other embodiments.

Turning now to FIGS. 2 to 4 , in addition to FIG. 1 , each of FIGS. 2 to4 illustrates a diagram of the example, non-limiting system 100 (FIG. 1) that can facilitate providing a defined parameter and determiningwhether to employ the defined parameter for a variational quantumalgorithm. Three different illustrations (e.g., at FIGS. 2, 3 and 4 )are provided detailing the parameter provision process resulting fromdifferent iterations of the aforementioned decision process performed bythe decision component 114. Repetitive description of like elementsand/or processes employed in the embodiment of FIG. 1 is omitted forsake of brevity.

First, referring first to FIGS. 2 to 4 collectively, the VQA component118, the Ansatz component 116 and the quantum system 130 can be referredto as the optimization portion 202 of the respective diagrams 200, 300and 400. The VQA component 118 can access the VQA 119, such as the VQA119 being directly accessed by, indirectly accessed by and/or stored atthe VQA component 118. Based on the VQA 119, the VQA component 118 candirect the provision, including searching, determination and/oroptimization, of one or more supplementary parameters by the Ansatzcomponent 116. That is, as shown, the VQA component 118 can communicatewith the Ansatz component 116 such as to direct output of increasinglyoptimized supplementary parameters, such as low energy parameters, bythe Ansatz component 116. Then, the VQA component 118 can directexecution of the VQA 119 on the quantum system 130.

The Ansatz component 116 can include a set of quantum circuits with oneor more free parameters. The Ansatz component 116 can approximate aquantum state of interest where free parameters take certain optimalvalues. Generally, interaction of the Ansatz component 116 with the VQAcomponent 118 enables finding one or more of these optimal values. Forexample, the Ansatz component 116 can employ an Ansatz method tooptimize a supplementary parameter, different from the defined parameter113, for running the VQA 119.

An Ansatz method can be selected by an entity, such as on the basis ofempirical, physical and/or chemical considerations that can beproblem-dependent. That is, the Ansatz component 116 can bepre-configured, such as by an entity and/or such as by being trained,such as employing the training component 108, to restrict the space ofall possible parameter configurations down to a sector of configurationsthat are known in advance, have optimal related properties, and/or makesense for use with the particular VQA 119. One or morepre-configurations can be entered by an entity, such as via a respectiveGUI accessing the parameter provision system 102 and/or the VQAcomponent 118.

Pre-configuring the Ansatz component 116 can involve choosing a set ofparametrized circuits and choosing values for the initial parametersdescribing those circuits. The set of parametrized circuits can beselected by an entity, such as on the basis of empirical, physicaland/or chemical considerations that can be problem-dependent. Initialparameters can be set to standard values (e.g., all zeros), randomvalues and/or values from a previous calculation. In one or more cases,as will be explained below with respect to FIG. 3 , an initial parametercan be the defined parameter 113 from the ML model 111, based on thedecision made by the decision component 114.

To then search for and/or optimize supplementary parameters, the Ansatzcomponent 116 can evaluate one or more associated cost functions and/orassociated gradient/hessian (e.g., first and/or second derivatives of agradient/hessian). That is, a cost function and/or a gradient/hessianthereof can be employed by a numerical optimizer (e.g., a conjugategradient) to identify one or more optimizations of the initialparameters aimed at lowering the value of the cost function. Measurementcan be performed regarding the cost function and its gradient/hessian.At the end of an optimization iteration, one or more objectives of theAnsatz component 116 are for the cost function to be lower than prior tothe optimization iteration, for the gradient to be zero (e.g., such thata stationary point has been reached) and/or for the hessian to bepositive (e.g., such that a local minimum has been reached).

Where one or more supplementary parameters are output from the Ansatzcomponent 116, these one or more supplementary parameters can be testedon the quantum system 130 to determine whether they are suitable, e.g.,optimal, for use in running the VQA 119. The quantum system 130 cancomprise any one or more suitable quantum devices and/or components,such as a quantum computer and/or a quantum calculation component 132.The quantum calculation component 132 can perform one or more quantumprocesses, calculations and/or measurements for executing one or morequantum algorithms, such as the VQA 119. The quantum system 130 can beconsidered part of the non-limiting system 100, with the non-limitingsystem 100 being a hybrid system. In other embodiments, the quantumsystem 130 can be separate from, but function in combination with, thenon-limiting system 100.

Such testing can include, for example, operating one or more quantumprograms and/or portions of programs and/or operating one or morequantum circuits. The testing can include running the VQA or a portionof the VQA.

In view of the testing of the one or more supplementary parameters onthe quantum system 130, the quantum system 130 can provide one or moreoutputs, such as one or more quantum measurements. For example, one ormore results of the testing can be compared and/or otherwise analyzed,such as to determine a parameter that best maximized a cost function ofthe VQA (e.g., VQA 119) and/or that better maximized a cost function ascompared to one or more previously employed parameters. It is noted thatoptimality of a parameter relative to maximizing a cost function can bedependent on the particular VQA to be run, such as a parameter being alow energy parameter or a low fidelity parameter.

As noted above, a goal of one type of VQA can be to maximize a costfunction in terms of energy. A goal of another type of VQA can be tofacilitate approximation of the action of a first quantum circuit over agiven wave function based upon the action of a second quantum circuitover the given wave function.

The VQA component 118 can direct further iterations of searching,optimization and/or testing of one or more parameters by the Ansatzcomponent 116 and/or the quantum system 130. Accordingly, through aplurality of iterations of these processes, the optimization portion 202can provide one or more optimized supplementary parameters to the VQAcomponent 118 for use in running the VQA 119 on the quantum system 130.

Furthermore, the central data store 109 can be communicatively connectedto the VQA component 118, such as directly, or indirectly in otherembodiments. In this way, the one or more supplementary parametersand/or associated metadata received from the Ansatz component 116,and/or the one or more measurement outputs received from the quantumsystem 130, can be added to the central data store 109.

Via one or more iterations of data added to the central data store 109,ML models trained by the training component 108, which can employ thecentral data store 109, can be increasingly better trained relative toone or more VQAs. In this way, the trained ML models can be increasinglycapable of providing optimal parameters relative to one or more VQAs.That is, the non-limiting system 100 can itself increase its futureoptimal output, such as its ability to provide increasingly optimaldefined parameters 113. Further, through the use of fewer resources foroptimizing parameter values for subsequent VQA operations, thenon-limiting system 100 can increase the throughput of the non-limitingsystem 100.

Referring now separately to FIG. 2 , apart from FIGS. 3 and 4 , adiagram 200 illustrates a first exemplary scenario of a performanceiteration of the parameter provision system 102. Based on at least theuncertainty prediction 115, such as failing to meet and/or exceed afirst uncertainty level and failing to meet and/or exceed a seconduncertainty level, the decision component 114 can make a first decision(e.g., decision (a) referred to above). The first decision can includethat the uncertainty prediction 115 has met neither of a firstuncertainty threshold nor a second uncertainty threshold. That is, theuncertainty prediction 115 can fail to meet the first uncertaintythreshold and be too high and thus does not meet and/or exceed thethreshold to bypass parameter optimization. The uncertainty prediction115 can also fail to meet and/or exceed the second uncertainty thresholdand thus be too high to proceed with optimization using the definedparameter 113 as the initial start point for the optimization portion202. That is, the decision component 114 can decide that the definedparameter 113 will not be utilized for running the VQA 119 and/or foroptimizing a supplementary parameter for running the VQA 119.

In such case, the decision component 114 can direct the Ansatz component116 to proceed with a method 204 comprising beginning a new parameterprovision and optimization process. The optimization portion 202 canproceed with the aforementioned one or more iterations, such asperforming an Ansatz-based optimization, with resulting data being addedto the central data store 109. Data related to the performance of the MLmodel 111 and/or from the results of iterations of the optimizationprocess performed can be added to the central data store 109 to allowfor future updated training of an ML model 111, such as via the VQAcomponent 118 and/or the updating component 120.

Looking next to FIG. 3 , the figure illustrates a diagram 300 of asecond exemplary scenario of a performance iteration of the parameterprovision system 102. Based on at least the uncertainty prediction 115,such as meeting and/or exceeding the first uncertainty level but not thesecond uncertainty level, the decision component 114 can make a seconddecision (e.g., decision (b) referred to above). The second decision caninclude that the uncertainty prediction does not meet and/or exceed thefirst uncertainty threshold and thus is too high to bypass furtheroptimization. However, in the case of diagram 300, the second decisioncan include that the uncertainty prediction 115 at least meets and/orexceeds the second uncertainty threshold for proceeding with parameteroptimization by the optimization portion 202 and using the definedparameter 113 as the initial start point.

That is, the decision component 114 can decide that the definedparameter 113 will not be utilized for running the VQA 119, but that thedefined parameter 113 can be utilized for accelerating the optimizationprocess performed by the optimization portion 202. For example, thedecision component 114 can direct the Ansatz component 116 to proceedwith method 304 comprising performing parameter optimization (e.g.,Ansatz-based optimization) using the defined parameter 113, instead ofsome other selected initial parameters, as an initial parameter for theoptimization. This itself can provide acceleration as compared toperforming a default Ansatz-based optimization.

As with method 204 described above, the optimization portion 202employing method 304 also can proceed with the aforementioned one ormore iterations of parameter optimization. Time and energy can be savedvia accelerating the optimization process performed by the optimizationportion 202 by providing a relevant starting point, i.e., the definedparameter 113. Also, data related to the performance of the ML model 111and from one or more results of one or more iterations of theoptimization process performed can be added to the central data store109, such as via the VQA component 118 and/or the updating component120, to allow for future updated training of an ML model 111.

Referring next to FIG. 4 , the figure illustrates a diagram 400 of athird exemplary scenario of a performance iteration of the parameterprovision system 102. Based on at least the uncertainty prediction 115,such as meeting and/or exceeding both the first uncertainty thresholdand the second uncertainty threshold, the decision component 114 canmake a third decision (e.g., decision (c) referred to above). The thirddecision can include that the uncertainty prediction 115 meets and/orexceeds the first uncertainty threshold to thus bypass furtheroptimization. The third decision also can include that the uncertaintyprediction 115 meets and/or exceeds the second uncertainty threshold tothus bypass accelerated parameter optimization. That is, the decisioncomponent 114 can determine that the defined parameter 113 itself caninstead be utilized for running the VQA 119.

In such case, the decision component 114 can direct the VQA component118 to proceed with method 404 and to utilize the defined parameter 113for running the VQA 119 without a need for conducting Ansatz-basedoptimization by Ansatz component 116. In such case, the Ansatz-basedoptimization process will not be performed by the optimization portion202, and the Ansatz component 116 can be unused by the method 404. Timeand energy can be saved via fully bypassing the optimization processperformed by the optimization portion 202. Also, data related to theperformance of the ML model 111, such as the defined parameter 113 andrelated uncertainty prediction 115, can be added to the central datastore 109, such as via the VQA component 118 and/or the updatingcomponent 120, to allow for future updated training of an ML model 111.

Looking again briefly to FIG. 1 , regardless of the method 204, 304 or404 directed by the decision component 114, the updating component 120can facilitate the addition of resultant data, such as the one or moredefined parameters 113, uncertainty prediction 115 and/or results fromthe optimization portion 202, to the central data store 109. That is,the updating component 120 can communicate with one or more componentsof the parameter provision system 102, such as the decision component114, performance component 112 and/or VQA component 118 to determinewhether one or more aspects of resultant data are available for beingadded to the central data store 109. As indicated above, this can allowfor future updated training of an ML model 111.

In one or more embodiments, the parameter provision system 102 caninclude an aggregation component 122 that can facilitate updating of thecentral data store 109 with one or more defined parameters, associateduncertainty predictions and/or a combination thereof from a plurality ofsystems. This plurality of systems can include one or more systemshaving an embodiment of a parameter provision system as described hereinor communicating with a system having an embodiment of a parameterprovision system as described herein. The systems of this plurality ofsystems can be distributed relative to one another. In one or moreembodiments, the systems of this plurality of systems can beadditionally and/or alternatively decentralized relative to one another.In one or more embodiments, one or more systems of this plurality ofsystems can communicate with the non-limiting system 100 (e.g., theparameter provision system 102 and/or the aggregation component 122)and/or directly with the central data store 109 via a network, such asvia the cloud. Via these one or more embodiments, scaled addition ofdata to the central data store 109 can be facilitated, allowing forbetter training of uncertainty aware ML models for providing parametersfor running a VQA. It thus will be appreciated that any number of thesystems of the aforementioned plurality of systems can employ the samecentral data store 109 to train respective ML models.

Turning now to FIGS. 5 through 7 , these figures together illustrate aflow diagram of an example, non-limiting computer-implemented method 500that can facilitate providing a defined parameter, determining whetherto employ the defined parameter for a variational quantum algorithm, andoperating the variational quantum algorithm on one or more qubits on aquantum system, in accordance with one or more embodiments describedherein. Repetitive description of like elements and/or processesemployed in respective embodiments is omitted for sake of brevity.

Looking first to 502 at FIG. 5 , the computer-implemented method 500 cancomprise determining, by a system (e.g., via parameter provision system102 and/or training component 108, and/or using central data store 109)operatively coupled to a processor (e.g., processor 104, a quantumprocessor and/or like processor), one or more aspects of data (e.g.,historical data) to employ for training of a machine learning (ML) model(e.g., ML model 111) directed to generation of a defined parameter(e.g., a variational parameter, such as the defined parameter 113) foruse by an algorithm (e.g., VQA 119).

Looking next to 504, the computer-implemented method 500 can comprisetraining, by a system (e.g., via parameter provision system 102 and/orperformance component 112, and/or using central data store 109), the MLmodel (e.g., ML model 111).

At 506, the computer-implemented method 500 can comprise operating, bythe system (e.g., via parameter provision system 102 and/or trainingcomponent 108), the ML model (e.g., ML model 111) to provide a definedparameter (e.g., defined parameter 113) for running the VQA (e.g., VQA119).

At 506, the computer-implemented method 500 also can comprise operating,by the system (e.g., via parameter provision system 102 and/or trainingcomponent 108), the ML model (e.g., ML model 111) to provide anuncertainty prediction (e.g., uncertainty prediction 115) related to thedefined parameter (e.g., defined parameter 113).

The computer-implemented method 500 can proceed from block 506 to eachof blocks 508 and 518, successively and/or at least partiallyconcurrently.

At 518, the computer-implemented method 500 can comprise updating, bythe system (e.g., via parameter provision system 102 and/or datacomponent 110), the central data store (e.g., central data store 109)with the results from the ML model (e.g., defined parameter 113 and/oruncertainty prediction 115 from the ML model 111).

At 508, the computer-implemented method 500 can comprise determining, bythe system (e.g., via parameter provision system 102 and/or decisioncomponent 114), how to use the defined parameter (e.g., definedparameter 113). Based on the uncertainty prediction (e.g., uncertaintyprediction 115) and one or more defined uncertainty thresholds, thecomputer-implemented method 500 can comprise determining, by a system(e.g., via parameter provision system 102 and/or decision component 114)to implement any of method “A”, method “B” or method “C” (e.g., methods204, 304 and 404, respectively).

Turning to FIG. 6 , this figure illustrates an extension of thecomputer-implemented method 500 of FIG. 5 . Triangle “A” 512 representsa continuation point for moving from FIG. 5 to FIG. 6 .

At 602, the computer-implemented method 500 can comprise performing, bythe system (e.g., via parameter provision system 102, optimizationportion 202, VQA component 118 and/or Ansatz component 116), parameteroptimization (e.g., Ansatz-based parameter optimization) that does notemploy the defined parameter (e.g., defined parameter 113). As discussedabove with respect to FIGS. 1 and 2 , the VQA component 118 can directthe Ansatz component 116 in parameter optimization.

Turning to 604, the computer-implemented method 500 can compriseproviding the results (e.g., one or more supplementary parameters) bythe system (e.g., via parameter provision system 102 and/or VQAcomponent 118), for being tested.

At 606, the computer-implemented method 500 can comprise testing, by thesystem (e.g., via parameter provision system 102, VQA component 118,quantum system 130 and/or quantum calculation component 132), the one ormore supplementary parameters to determine whether they are suitable,e.g., optimal, for use in running the VQA 119. Such testing can include,for example, operating one or more quantum programs and/or portions ofprograms and/or operating one or more quantum circuits. The testing caninclude running the VQA or a portion of the VQA. It will be appreciatedthat the quantum system 130 can be considered part of the non-limitingsystem 100 or the quantum system 130 can function in combination withthe non-limiting system 100.

At 608, the computer-implemented method 500 can comprise determining, bythe system (e.g., via parameter provision system 102 and/or VQAcomponent 118), whether or not to use the tested one or moresupplementary parameters for running the VQA (e.g., VQA 119). Forexample, one or more results of the testing can be compared and/orotherwise analyzed, such as to determine a parameter that best maximizeda cost function of the VQA (e.g., VQA 119) and/or that better maximizeda cost function as compared to one or more previously employedparameters.

Where the answer at the determination block 608 is no, at 610 thecomputer-implemented method 500 can comprise repeating, by the system(e.g., via parameter provision system 102 and/or VQA component 118), theoptimization process (e.g., Ansatz-based parameter optimization) usingdata related to the supplementary parameter from the current iteration.That is, the computer-implemented method 500 can re-execute theprocesses of blocks 602, 604, 606 and 608.

Where the answer at the determination block 608 is yes, at 612, thecomputer-implemented method 500 can comprise directing, by the system(e.g., via parameter provision system 102 and/or VQA component 118),running of the VQA (e.g., VQA 119) using the supplementary parameter.That is, the VQA (e.g., VQA 119) can be operated (e.g., via VQAcomponent 118, quantum system 130 and/or quantum calculation component132) on one or more qubits of a quantum system (e.g., quantum system130).

Then, at 518, the computer-implemented method 500 can comprise updating,by the system (e.g., via parameter provision system 102 and/or VQAcomponent 118), the central data store 109 with the resulting one ormore parameter iterations and/or quantum test results from theoptimization portion 202.

Turning now to FIG. 7 , this figure illustrates additional extensions ofthe computer-implemented method 500 of FIG. 5 . Triangles “B” 514 and“C” 516 each represent a continuation point for moving from FIG. 5 toFIG. 7 .

Looking first to the continuation triangle “B” 514, at 702, thecomputer-implemented method 500 can comprise performing, by the system(e.g., via parameter provision system 102, optimization portion 202, VQAcomponent 118 and/or Ansatz component 116), parameter optimization(e.g., Ansatz-based parameter optimization) that employs the definedparameter (e.g., defined parameter 113) as a starting point. Asdiscussed above with respect to FIGS. 1 and 3 , the VQA component 118can direct the Ansatz component 116 in parameter optimization.

Turning to 704, the computer-implemented method 500 can compriseproviding the results (e.g., one or more supplementary parameters) bythe system (e.g., via parameter provision system 102 and/or VQAcomponent 118), for being tested.

At 706, the computer-implemented method 500 can comprise testing, by thesystem (e.g., via parameter provision system 102, VQA component 118,quantum system 130 and/or quantum calculation component 132), the one ormore supplementary parameters to determine a suitable, e.g., optimal,parameter, such as relative to a maximizing a cost function of the VQA(e.g., VQA 119). For example, such testing can include, operating one ormore quantum programs and/or portions of programs and/or operating oneor more quantum circuits. The testing can include running the VQA or aportion of the VQA. It will be appreciated that the quantum system 130can be considered part of the non-limiting system 100 or that thequantum system 130 can function in combination with the non-limitingsystem 100.

At 708, the computer-implemented method 500 can comprise determining, bythe system (e.g., via parameter provision system 102 and/or VQAcomponent 118), whether or not to use the tested one or moresupplementary parameters for running the VQA (e.g., VQA 119). Forexample, one or more results of the testing can be compared and/orotherwise analyzed, such as to determine a parameter that best maximizeda cost function of the VQA (e.g., VQA 119) and/or that better maximizeda cost function as compared to one or more previously employedparameters.

Where the answer from the determination block 708 is no, at 710 thecomputer-implemented method 500 can comprise repeating, by the system(e.g., via parameter provision system 102 and/or VQA component 118), theoptimization process (e.g., Ansatz-based parameter optimization) usingdata related to the supplementary parameter from the current iteration.That is, the computer-implemented method 500 can re-execute theprocesses of blocks 702, 704, 706 and 708.

Where the answer from the determination block 708 is yes, at 612, thecomputer-implemented method 500 can comprise directing, by a system(e.g., via parameter provision system 102 and/or VQA component 118),running of the VQA (e.g., VQA 119) using the supplementary parameter.That is, the VQA (e.g., VQA 119) can be operated (e.g., via VQAcomponent 118, quantum system 130 and/or quantum calculation component132) on one or more qubits of a quantum system (e.g., quantum system130).

Then, at 518, the computer-implemented method 500 can comprise updating,by a system (e.g., via parameter provision system 102 and/or VQAcomponent 118), the central data store 109 with the resulting one ormore parameter iterations and/or quantum test results from theoptimization portion 202.

Looking still to FIG. 7 , and now to continuation triangle “C” 516, thecomputer-implemented method 500 can comprise bypassing, by the system(e.g., via parameter provision system 102 and/or VQA component 118), anyparameter optimization (e.g., parameter optimization) that employs ordoes not employ the defined parameter (e.g., defined parameter 113). Asdiscussed above with respect to FIGS. 1 and 4 , the VQA component 118via this process does not direct the Ansatz component 116 in parameteroptimization.

Next, at 612, the computer-implemented method 500 can comprisedirecting, by a system (e.g., via parameter provision system 102 and/orVQA component 118), running of the VQA (e.g., VQA 119) using the definedparameter (e.g., defined parameter 113).

Then, at 518, the computer-implemented method 500 can comprise updating,by a system (e.g., via parameter provision system 102 and/or VQAcomponent 118), the central data store 109 with the resulting one ormore parameter iterations from the optimization portion 202.

For simplicity of explanation, the computer-implemented methodologiesare depicted and described as a series of acts. It is to be understoodand appreciated that the subject innovation is not limited by the actsillustrated and/or by the order of acts, for example acts can occur invarious orders and/or concurrently, and with other acts not presentedand described herein. Furthermore, not all illustrated acts can berequired to implement the computer-implemented methodologies inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the computer-implementedmethodologies could alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, itshould be further appreciated that the computer-implementedmethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring the computer-implemented methodologies tocomputers. The term article of manufacture, as used herein, is intendedto encompass a computer program accessible from any computer-readabledevice or storage media.

In the above examples, it should be appreciated that one or moreembodiments described herein can enable technical improvements to aprocessing unit (e.g., processor 104) associated with parameterprovision system 102. By reducing and/or altogether bypassingsupplementary optimization of a parameter prior to its implementation ina VQA, the parameter provision system 102 can thereby facilitateimproved performance, improved efficiency and/or reduced computationalcost associated with a processing unit (e.g., processor 104) employingthe parameter provision system 102. That is, through the use of fewerresources for optimizing parameter values for subsequent VQA operations,the non-limiting system 100 can increase the throughput of thenon-limiting system 100. Further, by providing parameters having highusability, such as being low energy or low fidelity parameters, the VQAcan be run more efficiently, using less energy and/or time, for example,and thus resulting in respectively reduced error during operation on oneor more qubits of a quantum system.

It should also be appreciated that the parameter provision system 102can enable scaling of parameter provision, including parameteroptimization, and/or execution of related VQAs. That is, where eachparameter provision enables reduced time and energy, additionalparameter provisions and/or implementations of VQAs can be performedconcurrently and/or subsequently relative to one another.

In one or more instances, one or more embodiments as described hereincan integrate the disclosed teachings into a practical application.Indeed, as described herein, one or more embodiments, which can take theform of systems, computer-implemented methods, and/or computer programproducts can be considered as a computerized tool that can receive as aninput one or more data aspects and that can generate as an output aparameter (e.g., a variational parameter for a VQA) and an uncertaintyprediction relative to the parameter. More specifically, thecomputerized tool can generate an uncertainty aware machine model tofacilitate a determination as to how and/or whether or not to direct useof the defined parameter by the VQA. This determination can reduceand/or bypass parameter optimization via an Ansatz method. Furthermorevia use of the associated central data store, the non-limiting system100 can itself increase its future optimal output, such as its abilityto provide increasingly improved defined parameters. This is a usefuland practical application of computers, especially in view of the costlynature (e.g., in terms of time, energy and/or fidelity) of parameterprovision, including optimization, for quantum computation. Overall,such computerized tools constitute a concrete and tangible technicalimprovement in the field of quantum parameter provision.

Additionally, it is noted that one or more embodiments described hereincan control real-world devices based on the disclosed teachings. Forexample, embodiments described herein can receive as input real-worlddata aspects, such as including historical data, and can generate asoutput a defined parameter (e.g., a variational parameter for a VQA) andan uncertainty prediction relative to the defined parameter. The definedparameter can be utilized to operate the associated VQA on or morequbits of a real-world quantum computing device. That is, one or moreembodiments described herein can execute such real-world definedparameters on a real-world quantum computing device.

It is to be appreciated that one or more embodiments described hereincan employ hardware and/or software to solve problems that are highlytechnical in nature (e.g., related to generating a variational quantumparameter for running a quantum algorithm), that are not abstract, andthat cannot be performed as a set of mental acts by a human. Forexample, a human, or even thousands of humans, cannot efficiently,accurately and/or effectively provide, such as including optimizationof, a parameter for a variational quantum algorithm in the time that oneor more embodiments described herein can facilitate this process. And,neither the human mind nor a human with pen and paper can electronicallyprovide a variational quantum parameter, associated uncertaintyprediction and/or subsequent optimization.

That is, one or more embodiments described herein are inherently andinextricably tied to computer technology and cannot be implementedoutside of a hybrid classical/quantum computing environment. Forexample, one or more processes performed by one or more embodimentsdescribed herein can more efficiently provide these parameters ascompared to current systems and/or techniques. Systems,computer-implemented methods and/or computer program productsfacilitating performance of these processes are of great utility in thefield of quantum computation and cannot be equally practicablyimplemented in a sensible way outside of a computing environment.

In one or more embodiments, one or more of the processes describedherein can be performed by one or more specialized computers (e.g., aspecialized processing unit, a specialized classical computer, aspecialized quantum computer, a specialized hybrid classical/quantumsystem and/or another type of specialized computer) to execute definedtasks related to the various technologies describe above. One or moreembodiments described herein and/or components thereof, can be employedto solve new problems that arise through advancements in technologiesmentioned above, employment of quantum computing systems, cloudcomputing systems, computer architecture and/or another technology.

One or more embodiments described herein can be fully operationaltowards performing one or more other functions (e.g., fully powered on,fully executed and/or another function) while also performing the one ormore operations described herein.

In order to provide additional context for one or more embodimentsdescribed herein, FIG. 8 and the following discussion are intended toprovide a brief, general description of a suitable operating environment800 in which the one or more embodiments described herein can beimplemented. For example, one or more components and/or other aspects ofembodiments described herein can be implemented in or be associated withthe operating environment 800. Further, while one or more embodimentshave been described above in the general context of computer-executableinstructions that can run on one or more computers, those skilled in theart will recognize that the embodiments can be also implemented incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules include routines, programs, components, datastructures and/or the like, that perform particular tasks and/orimplement particular abstract data types. Moreover, those skilled in theart will appreciate that the inventive methods can be practiced withother computer system configurations, including single-processor ormultiprocessor computer systems, minicomputers, mainframe computers,Internet of Things (IoT) devices, distributed computing systems, as wellas personal computers, hand-held computing devices, microprocessor-basedor programmable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage mediaand/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,but not limitation, computer-readable storage media and/ormachine-readable storage media can be implemented in connection with anymethod or technology for storage of information such ascomputer-readable and/or machine-readable instructions, program modules,structured data and/or unstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD ROM), digitalversatile disk (DVD), Blu-ray disc (BD) and/or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage and/orother magnetic storage devices, solid state drives or other solid statestorage devices and/or other tangible and/or non-transitory media whichcan be used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory and/or computer-readable mediathat are not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries and/orother data retrieval protocols, for a variety of operations with respectto the information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, but not limitation, communication media can include wiredmedia, such as a wired network, direct-wired connection and/or wirelessmedia such as acoustic, RF, infrared and/or other wireless media.

With reference again to FIG. 8 , the example operating environment 800for implementing one or more embodiments of the aspects described hereincan include a computer 802, the computer 802 including a processing unit804, a system memory 806 and/or a system bus 808. It will be appreciatedthat any aspect of the system memory 806 or processing unit 804 can beapplied to memory 106 or processor 104, respectively of the non-limitingsystem 100 and/or can be implemented in combination and/or alternativelyto memory 106 or processor 104, respectively.

Memory 806 can store one or more computer and/or machine readable,writable and/or executable components and/or instructions that, whenexecuted by processing unit 804 (e.g., a classical processor, a quantumprocessor and/or like processor), can facilitate performance ofoperations defined by the executable component(s) and/or instruction(s).For example, memory 806 can store computer and/or machine readable,writable and/or executable components and/or instructions that, whenexecuted by processing unit 804, can facilitate execution of the variousfunctions described herein relating to non-limiting system 100 and/orparameter provision system 102, as described herein with or withoutreference to the various figures of the one or more embodiments.

Memory 806 can comprise volatile memory (e.g., random access memory(RAM), static RAM (SRAM), dynamic RAM (DRAM) and/or the like) and/ornon-volatile memory (e.g., read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM) and/or the like) that can employ one or morememory architectures.

Processing unit 804 can comprise one or more types of processors and/orelectronic circuitry (e.g., a classical processor, a quantum processorand/or like processor) that can implement one or more computer and/ormachine readable, writable and/or executable components and/orinstructions that can be stored at memory 806. For example, processingunit 804 can perform one or more operations that can be specified bycomputer and/or machine readable, writable and/or executable componentsand/or instructions including, but not limited to, logic, control,input/output (I/O), arithmetic and/or the like. In one or moreembodiments, processing unit 804 can be any of various commerciallyavailable processors. In one or more embodiments, processing unit 804can comprise one or more central processing unit, multi-core processor,microprocessor, dual microprocessors, microcontroller, System on a Chip(SOC), array processor, vector processor, quantum processor and/oranother type of processor. The examples of processing unit 804 can beemployed to implement any one or more embodiments described herein.

The system bus 808 can couple system components including, but notlimited to, the system memory 806 to the processing unit 804. The systembus 808 can be any of several types of bus structure that can furtherinterconnect to a memory bus (with or without a memory controller), aperipheral bus and/or a local bus using any of a variety of commerciallyavailable bus architectures. The system memory 806 can include ROM 810and/or RAM 812. A basic input/output system (BIOS) can be stored in anon-volatile memory such as ROM, erasable programmable read only memory(EPROM) and/or EEPROM, which BIOS contains the basic routines that helpto transfer information among elements within the computer 802, such asduring startup. The RAM 812 can also include a high-speed RAM, such asstatic RAM for caching data.

The computer 802 further can include an internal hard disk drive (HDD)814 (e.g., EIDE, SATA), one or more external storage devices 816 (e.g.,a magnetic floppy disk drive (FDD), a memory stick or flash drivereader, a memory card reader and/or the like) and/or a drive 820, e.g.,such as a solid state drive or an optical disk drive, which can read orwrite from a disk 822, such as a CD-ROM disc, a DVD, a BD and/or thelike. Additionally and/or alternatively, where a solid state drive isinvolved, disk 822 could not be included, unless separate. While theinternal HDD 814 is illustrated as located within the computer 802, theinternal HDD 814 can also be configured for external use in a suitablechassis (not shown). Additionally, while not shown in operatingenvironment 800, a solid state drive (SSD) could be used in addition to,or in place of, an HDD 814. The HDD 814, external storage device(s) 816and drive 820 can be connected to the system bus 808 by an HDD interface824, an external storage interface 826 and a drive interface 828,respectively. The HDD interface 824 for external drive implementationscan include at least one or both of Universal Serial Bus (USB) andInstitute of Electrical and Electronics Engineers (IEEE) 1394 interfacetechnologies. Other external drive connection technologies are withincontemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 802, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto respective types of storage devices, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, whether presently existing or developed in thefuture, could also be used in the example operating environment, andfurther, that any such storage media can contain computer-executableinstructions for performing the methods described herein.

A number of program modules can be stored in the drives and RAM 812,including an operating system 830, one or more applications 832, otherprogram modules 834 and/or program data 836. All or portions of theoperating system, applications, modules and/or data can also be cachedin the RAM 812. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsand/or combinations of operating systems.

Computer 802 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 830, and the emulated hardwarecan optionally be different from the hardware illustrated in FIG. 8 . Ina related embodiment, operating system 830 can comprise one virtualmachine (VM) of multiple VMs hosted at computer 802. Furthermore,operating system 830 can provide runtime environments, such as the JAVAruntime environment or the .NET framework, for applications 832. Runtimeenvironments are consistent execution environments that allowapplications 832 to run on any operating system that includes theruntime environment. Similarly, operating system 830 can supportcontainers, and applications 832 can be in the form of containers, whichare lightweight, standalone, executable packages of software thatinclude, e.g., code, runtime, system tools, system libraries and/orsettings for an application.

Further, computer 802 can be enabled with a security module, such as atrusted processing module (TPM). For instance, with a TPM, bootcomponents hash next in time boot components and wait for a match ofresults to secured values before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 802, e.g., applied at application execution level and/or atoperating system (OS) kernel level, thereby enabling security at anylevel of code execution.

An entity can enter and/or transmit commands and information into thecomputer 802 through one or more wired/wireless input devices, e.g., akeyboard 838, a touch screen 840 and/or a pointing device, such as amouse 842. Other input devices (not shown) can include a microphone, aninfrared (IR) remote control, a radio frequency (RF) remote control, orother remote control, a joystick, a virtual reality controller and/orvirtual reality headset, a game pad, a stylus pen, an image inputdevice, e.g., camera(s), a gesture sensor input device, a visionmovement sensor input device, an emotion or facial detection device, abiometric input device, e.g., fingerprint or iris scanner, or the like.These and other input devices can be connected to the processing unit804 through an input device interface 844 that can be coupled to thesystem bus 808, but can be connected by other interfaces, such as aparallel port, an IEEE 1394 serial port, a game port, a USB port, an IRinterface, a BLUETOOTH® interface and/or the like.

A monitor 846 or other type of display device can be alternativelyand/or additionally connected to the system bus 808 via an interface,such as a video adapter 848. In addition to the monitor 846, a computertypically includes other peripheral output devices (not shown), such asspeakers, printers and/or the like.

The computer 802 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 850. The remotecomputer(s) 850 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device and/or other common network node, and typicallyincludes many or all of the elements described relative to the computer802, although, for purposes of brevity, only a memory/storage device 852is illustrated. Additionally and/or alternatively, the computer 802 canbe coupled (e.g., communicatively, electrically, operatively, opticallyand/or the like) to one or more external systems, sources and/or devices(e.g., classical and/or quantum computing devices, communication devicesand/or like device) via a data cable (e.g., High-Definition MultimediaInterface (HDMI), recommended standard (RS) 232, Ethernet cable and/orthe like).

In one or more embodiments, a network can comprise one or more wiredand/or wireless networks, including, but not limited to, a cellularnetwork, a wide area network (WAN) (e.g., the Internet), or a local areanetwork (LAN). For example, one or more embodiments described herein cancommunicate with one or more external systems, sources and/or devices,for instance, computing devices (and vice versa) using virtually anydesired wired or wireless technology, including but not limited to:wireless fidelity (Wi-Fi), global system for mobile communications(GSM), universal mobile telecommunications system (UMTS), worldwideinteroperability for microwave access (WiMAX), enhanced general packetradio service (enhanced GPRS), third generation partnership project(3GPP) long term evolution (LTE), third generation partnership project 2(3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA),Zigbee and other 802.XX wireless technologies and/or legacytelecommunication technologies, BLUETOOTH®, Session Initiation Protocol(SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6over Low power Wireless Area Networks), Z-Wave, an ANT, anultra-wideband (UWB) standard protocol and/or other proprietary and/ornon-proprietary communication protocols. In a related example, one ormore embodiment described herein can include hardware (e.g., a centralprocessing unit (CPU), a transceiver, a decoder, quantum hardware, aquantum processor and/or the like), software (e.g., a set of threads, aset of processes, software in execution, quantum pulse schedule, quantumcircuit, quantum gates and/or the like) and/or a combination of hardwareand software that facilitates communicating information among one ormore embodiments described herein and external systems, sources and/ordevices (e.g., computing devices, communication devices and/or thelike).

The logical connections depicted include wired/wireless connectivity toa local area network (LAN) 854 and/or larger networks, e.g., a wide areanetwork (WAN) 856. LAN and WAN networking environments are commonplacein offices and companies, and facilitate enterprise-wide computernetworks, such as intranets, all of which can connect to a globalcommunications network, e.g., the Internet.

When used in a LAN networking environment, the computer 802 can beconnected to the local network 854 through a wired and/or wirelesscommunication network interface or adapter 858. The adapter 858 canfacilitate wired or wireless communication to the LAN 854, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 858 in a wireless mode.

When used in a WAN networking environment, the computer 802 can includea modem 860 and/or can be connected to a communications server on theWAN 856 via other means for establishing communications over the WAN856, such as by way of the Internet. The modem 860, which can beinternal or external and a wired and/or wireless device, can beconnected to the system bus 808 via the input device interface 844. In anetworked environment, program modules depicted relative to the computer802 or portions thereof, can be stored in the remote memory/storagedevice 852. It will be appreciated that the network connections shownare example and other means of establishing a communications link amongthe computers can be used.

When used in either a LAN or WAN networking environment, the computer802 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 816 asdescribed above, such as but not limited to, a network virtual machineproviding one or more aspects of storage or processing of information.Generally, a connection between the computer 802 and a cloud storagesystem can be established over a LAN 854 or WAN 856 e.g., by the adapter858 or modem 860, respectively. Upon connecting the computer 802 to anassociated cloud storage system, the external storage interface 826 can,with the aid of the adapter 858 and/or modem 860, manage storageprovided by the cloud storage system as it would other types of externalstorage. For instance, the external storage interface 826 can beconfigured to provide access to cloud storage sources as if thosesources were physically connected to the computer 802.

The computer 802 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, telephone and/or any piece ofequipment or location associated with a wirelessly detectable tag (e.g.,a kiosk, news stand, store shelf and/or the like). This can includeWireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus,the communication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

The illustrated embodiments described herein can be also practiced indistributed computing environments (e.g., cloud computing environments),such as described below with respect to FIG. 9 , where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located both in local and remote memory storage devices.

For example, one or more embodiments described herein and/or one or morecomponents thereof can employ one or more computing resources of thecloud computing environment 950 described below with reference to FIG. 9, and/or with reference to the one or more functional abstraction layers(e.g., quantum software and/or the like) described below with referenceto FIG. 10 , to execute one or more operations in accordance with one ormore embodiments described herein. For example, cloud computingenvironment 950 and/or one or more of the functional abstraction layers1060, 1070, 1080 and/or 1090 can comprise one or more classicalcomputing devices (e.g., classical computer, classical processor,virtual machine, server and/or the like), quantum hardware and/orquantum software (e.g., quantum computing device, quantum computer,quantum processor, quantum circuit simulation software, superconductingcircuit and/or the like) that can be employed by one or more embodimentsdescribed herein and/or components thereof to execute one or moreoperations in accordance with one or more embodiments described herein.For instance, one or more embodiments described herein and/or componentsthereof can employ such one or more classical and/or quantum computingresources to execute one or more classical and/or quantum: mathematicalfunction, calculation and/or equation; computing and/or processingscript; algorithm; model (e.g., artificial intelligence (AI) model,machine learning (ML) model and/or like model); and/or another operationin accordance with one or more embodiments described herein.

It is to be understood that although one or more embodiments describedherein include a detailed description on cloud computing, implementationof the teachings recited herein are not limited to a cloud computingenvironment. Rather, one or more embodiments described herein arecapable of being implemented in conjunction with any other type ofcomputing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model can includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but can specify location at a higher level ofabstraction (e.g., country, state or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning can appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth and active user accounts). Resource usage can bemonitored, controlled and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage orindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks and/or otherfundamental computing resources where the consumer can deploy and runarbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications and/or possibly limited control of selectnetworking components (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It can be managed by the organization or a third party andcan exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy and/or complianceconsiderations). It can be managed by the organizations or a third partyand can exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing among clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity and/or semanticinteroperability. At the heart of cloud computing is an infrastructurethat includes a network of interconnected nodes.

Moreover, the non-limiting system 100 and/or the example operatingenvironment 800 can be associated with or be included in a dataanalytics system, a data processing system, a graph analytics system, agraph processing system, a big data system, a social network system, aspeech recognition system, an image recognition system, a graphicalmodeling system, a bioinformatics system, a data compression system, anartificial intelligence system, an authentication system, a syntacticpattern recognition system, a medical system, a health monitoringsystem, a network system, a computer network system, a communicationsystem, a router system, a server system, a high availability serversystem (e.g., a Telecom server system), a Web server system, a fileserver system, a data server system, a disk array system, a poweredinsertion board system, a cloud-based system or the like. In accordancetherewith, non-limiting system 100 and/or example operating environment800 can be employed to use hardware and/or software to solve problemsthat are highly technical in nature, that are not abstract and/or thatcannot be performed as a set of mental acts by a human.

Referring still to FIG. 9 , the illustrative cloud computing environment950 is depicted. As shown, cloud computing environment 950 includes oneor more cloud computing nodes 910 with which local computing devicesused by cloud consumers, such as, for example, personal digitalassistant (PDA) or cellular telephone 954A, desktop computer 954B,laptop computer 954C and/or automobile computer system 954N cancommunicate. Although not illustrated in FIG. 9 , cloud computing nodes910 can further comprise a quantum platform (e.g., quantum computer,quantum hardware, quantum software and/or the like) with which localcomputing devices used by cloud consumers can communicate. Cloudcomputing nodes 910 can communicate with one another. They can begrouped (not shown) physically or virtually, in one or more networks,such as Private, Community, Public, or Hybrid clouds as describedhereinabove, or a combination thereof. This allows cloud computingenvironment 950 to offer infrastructure, platforms and/or software asservices for which a cloud consumer does not need to maintain resourceson a local computing device. It is understood that the types ofcomputing devices 954A-N shown in FIG. 9 are intended to be illustrativeonly and that cloud computing nodes 910 and cloud computing environment950 can communicate with any type of computerized device over any typeof network and/or network addressable connection (e.g., using a webbrowser).

Referring now to FIG. 10 , a set of functional abstraction layers isshown, such as provided by cloud computing environment 950 (FIG. 9 ).One or more embodiments described herein can be associated with one ormore functional abstraction layers described below with reference toFIG. 10 (e.g., hardware and software layer 1060, virtualization layer1070, management layer 1080 and/or workloads layer 1090). It should beunderstood in advance that the components, layers and functions shown inFIG. 10 are intended to be illustrative only and embodiments describedherein are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 1060 can include hardware and softwarecomponents. Examples of hardware components include: mainframes 1061;RISC (Reduced Instruction Set Computer) architecture-based servers 1062;servers 1063; blade servers 1064; storage devices 1065; and networks andnetworking components 1066. In one or more embodiments, softwarecomponents can include network application server software 1067, quantumplatform routing software 1068 and/or quantum software (not illustratedin FIG. 10 ).

Virtualization layer 1070 can provide an abstraction layer from whichthe following examples of virtual entities can be provided: virtualservers 1071; virtual storage 1072; virtual networks 1073, includingvirtual private networks; virtual applications and/or operating systems1074; and/or virtual clients 1075.

In one example, management layer 1080 can provide the functionsdescribed below. Resource provisioning 1081 can provide dynamicprocurement of computing resources and other resources that can beutilized to perform tasks within the cloud computing environment.Metering and Pricing 1082 can provide cost tracking as resources areutilized within the cloud computing environment, and billing orinvoicing for consumption of these resources. In one example, theseresources can include application software licenses. Security canprovide identity verification for cloud consumers and tasks, as well asprotection for data and other resources. User (or entity) portal 1083can provide access to the cloud computing environment for consumers andsystem administrators. Service level management 1084 can provide cloudcomputing resource allocation and management such that required servicelevels are met. Service Level Agreement (SLA) planning and fulfillment1085 can provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 1090 can provide examples of functionality for which thecloud computing environment can be utilized. Non-limiting examples ofworkloads and functions which can be provided from this layer include:mapping and navigation 1091; software development and lifecyclemanagement 1092; virtual classroom education delivery 1093; dataanalytics processing 1094; transaction processing 1095; and/orapplication transformation software 1096.

The embodiments described herein can be directed to one or more of asystem, a method, an apparatus and/or a computer program product at anypossible technical detail level of integration. The computer programproduct can include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the one or more embodiments described herein.The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device and/or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium can also include the following: aportable computer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon and/or any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of the one or more embodimentsdescribed herein can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, and/orsource code and/or object code written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Smalltalk, C++ or the like, and/or procedural programminglanguages, such as the “C” programming language and/or similarprogramming languages. The computer readable program instructions canexecute entirely on a computer, partly on a computer, as a stand-alonesoftware package, partly on a computer and/or partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer can be connected to a computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection can be made to an external computer(for example, through the Internet using an Internet Service Provider).In one or more embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA)and/or programmable logic arrays (PLA) can execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the one or more embodiments describedherein.

Aspects of the one or more embodiments described herein are describedwith reference to flowchart illustrations and/or block diagrams ofmethods, apparatus (systems), and computer program products according toone or more embodiments described herein. It will be understood thateach block of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer readable program instructions.These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer and/orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks. The computer readable program instructions can also be loadedonto a computer, other programmable data processing apparatus and/orother device to cause a series of operational acts to be performed onthe computer, other programmable apparatus or other device to produce acomputer implemented process, such that the instructions which executeon the computer, other programmable apparatus and/or other deviceimplement the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, computer-implementable methods and/or computer programproducts according to one or more embodiments described herein. In thisregard, each block in the flowchart or block diagrams can represent amodule, segment and/or portion of instructions, which comprises one ormore executable instructions for implementing the specified logicalfunction(s). In one or more alternative implementations, the functionsnoted in the blocks can occur out of the order noted in the Figures. Forexample, two blocks shown in succession can, in fact, be executedsubstantially concurrently, or the blocks can sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that the one or more embodiments herein also can beimplemented in combination with other program modules. Generally,program modules include routines, programs, components, data structuresand/or the like that perform particular tasks and/or implementparticular abstract data types. Moreover, those skilled in the art willappreciate that the inventive computer-implemented methods can bepracticed with other computer system configurations, includingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as computers, hand-held computingdevices (e.g., PDA, phone), microprocessor-based or programmableconsumer or industrial electronics and/or the like. The illustratedaspects can also be practiced in distributed computing environments inwhich tasks are performed by remote processing devices that are linkedthrough a communications network. However, some, if not all aspects ofthe one or more embodiments can be practiced on stand-alone computers.In a distributed computing environment, program modules can be locatedin both local and remote memory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and/or the like, can refer to and/or caninclude a computer-related entity or an entity related to an operationalmachine with one or more specific functionalities. The entitiesdisclosed herein can be either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentcan be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a programand/or a computer. By way of illustration, both an application runningon a server and the server can be a component. One or more componentscan reside within a process and/or thread of execution and a componentcan be localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, where the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and/or gates, in order to optimize spaceusage and/or to enhance performance of related equipment. A processorcan be implemented as a combination of computing processing units.

Herein, terms such as “store,” “storage,” “data store,” data storage,”“database,” and substantially any other information storage componentrelevant to operation and functionality of a component are utilized torefer to “memory components,” entities embodied in a “memory,” orcomponents comprising a memory. It is to be appreciated that memoryand/or memory components described herein can be either volatile memoryor nonvolatile memory or can include both volatile and nonvolatilememory. By way of illustration, and not limitation, nonvolatile memorycan include read only memory (ROM), programmable ROM (PROM),electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory and/or nonvolatile random access memory (RAM)(e.g., ferroelectric RAM (FeRAM). Volatile memory can include RAM, whichcan act as external cache memory, for example. By way of illustrationand not limitation, RAM is available in many forms such as synchronousRAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double datarate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM(SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM)and/or Rambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing the one or more embodiments, but oneof ordinary skill in the art can recognize that many furthercombinations and permutations of the one or more embodiments arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

The descriptions of the one or more embodiments have been presented forpurposes of illustration but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: a decision component thatdetermines, based upon an uncertainty prediction regarding the usabilityof a defined parameter that has been output from a machine learningmodel, whether to employ the defined parameter for running a variationalquantum algorithm.
 2. The system of claim 1, further comprising: aperformance component that executes the machine learning model toprovide the uncertainty prediction and the defined parameter, whereinthe defined parameter is a variational parameter for initialization ofthe variational quantum algorithm.
 3. The system of claim 1, furthercomprising: a training component that trains the machine learning modelby employing a central data store having data related to the variationalquantum algorithm.
 4. The system of claim 3, further comprising: anupdating component that updates the central data store with the definedparameter and the associated uncertainty prediction.
 5. The system ofclaim 3, further comprising: an aggregation component that enablesupdating of the central data store with one or more other definedparameters, other associated uncertainty predictions, or a combinationthereof, from a plurality of systems being distributed relative to oneanother.
 6. The system of claim 1, further comprising: a quantumcalculation component that executes the variational quantum algorithm ona quantum device, wherein the variational quantum algorithm employs oneor more parameters determined at least in part based on thedetermination regarding the defined parameter.
 7. The system of claim 1,further comprising: an Ansatz component that employs an Ansatz method tooptimize a supplementary parameter where the decision componentdetermines that the defined parameter will not be employed by thevariational quantum algorithm.
 8. A computer-implemented method,comprising: determining, by a system operatively coupled to a processor,and based upon an uncertainty prediction regarding the usability of adefined parameter having been output from a machine learning model,whether to employ the defined parameter for running a variationalquantum algorithm.
 9. The computer-implemented method of claim 8,further comprising: executing, by the system, the machine learning modelto provide the uncertainty prediction and the defined parameter, whereinthe defined parameter is a variational parameter for initialization ofthe variational quantum algorithm.
 10. The computer-implemented methodof claim 8, training, by the system, the machine learning model byemploying, by the system, a central data store having data related tothe variational quantum algorithm.
 11. The computer-implemented methodof claim 10, further comprising: updating, by the system, the centraldata store with the defined parameter and the associated uncertaintyprediction.
 12. The computer-implemented method of claim 10, furthercomprising: enabling, by the system, updating of the central data storewith one or more other defined parameters, other associated uncertaintypredictions, or a combination thereof, from a plurality of systems beingdistributed relative to one another.
 13. The computer-implemented methodof claim 8, further comprising: executing, by the system, thevariational quantum algorithm on a quantum device, including employing,by the system, one or more parameters, by the variational quantumalgorithm, determined at least in part based on the determinationregarding the defined parameter.
 14. The computer-implemented method ofclaim 8, further comprising: employing, by the system, an Ansatz methodto optimize a supplementary parameter where the system determines thatthe defined parameter will not be employed by the variational quantumalgorithm.
 15. A computer program product facilitating a processproviding a defined parameter and determining whether to employ thedefined parameter for a variational quantum algorithm, the computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith, the program instructionsexecutable by a processor to cause the processor to: determine, by theprocessor, and based upon an uncertainty prediction regarding theusability of the defined parameter having been output from a machinelearning model, whether to employ the defined parameter for running avariational quantum algorithm.
 16. The computer program product of claim15, further comprising causing the processor to: executes, by theprocessor, the machine learning model to provide the uncertaintyprediction and the defined parameter, wherein the defined parameter is avariational parameter for initialization of the variational quantumalgorithm.
 17. The computer program product of claim 15, furthercomprising causing the processor to: train, by the processor, themachine learning model by employing, by the system, a central data storehaving data related to the variational quantum algorithm.
 18. Thecomputer program product of claim 17, further comprising causing theprocessor to: update, by the processor, the central data store with thedefined parameter and the associated uncertainty prediction.
 19. Thecomputer program product of claim 17, further comprising causing theprocessor to: enable, by the processor, updating of the central datastore with one or more other defined parameters, other associateduncertainty predictions, or a combination thereof, from a plurality ofsystems being distributed relative to one another.
 20. The computerprogram product of claim 15, further comprising causing the processorto: execute, by the processor, the variational quantum algorithm on aquantum device, including employing, by the processor, one or moreparameters, by the variational quantum algorithm, determined at least inpart based on the determination regarding the defined parameter.